Altered cerebral gray matter volume and functional connectivity in patients with residual dizziness of benign paroxysmal positional vertigo.

Aim: To provide a theoretical basis for the study of the pathogenesis of residual dizziness (RD) from the perspective of imaging.

Materials And Methods: The general clinical data of the RD group and healthy control (HC) group were statistically analysed by two independent sample t tests, rank sum tests or chi-square tests. The imaging data of the two groups of people were preprocessed and statistically analysed by using the data processing and analysis for brain imaging (DPABI) software package.

NOTCH3 Mutation Causes Glymphatic Impairment and Promotes Brain Senescence in CADASIL.

Aims: The aim of this study is to investigate the role of glymphatic function of cerebral autosomal dominant arteriopathy, subcortical infarcts, and leukoencephalopathy (CADASIL), the most common monogenic small vessel disease caused by NOTCH3 mutation, and to explore potential therapeutic strategies to improve glymphatic function.

Methods: We assessed glymphatic influx and efflux function in CADASIL mouse models (Notch3) and correlated these findings with brain atrophy in CADASIL patients. We also investigated the underlying mechanisms of glymphatic impairment, focusing the expression of AQP4 in astrocytic endfeet.

One-step green synthesis durable flame-retardant, antibacterial and dyeable cellulose fabrics with a recyclable deep eutectic solvent.

The development of cellulose fabrics with good flame retardancy and durability has been a primary concern for in firefighting clothing. A recyclable ternary deep eutectic solvent (TDES) was used to prepare surface ammonium phosphate-modified cellulose fabrics (SACF). The incorporation of ammonium phosphate groups notably enhanced the durable flame retardancy of cellulose fabrics.

KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters.

Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation.